Welcome to w4repp’s documentation!

Welcome to the Wind for renewable energy production prediction (W4Repp) project page. This project is funded by ESA through contract 4000136600/21/I-DT-Ir under the Future EO-1 EO Science for Society programme.

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Maximaal Vermogen Bron:@WesterMeerWind

Note

At the end of the project it was concluded that the assimilation system did show that there was some sensitivity to the specifics of the assimilaiton dataset, but this was not very strong. It was speculated that this was because of the low spread in the ensemble. The ensemble was generated through a combination of two physical packages (CONUS and TROPICAL) and stochastic perturbations of the dynamics (through SKEB) and the physics (by SPPT). To test this hypothesis a large ensemble was generated through a combination of multiple initial conditions, two physical packages (CONUS and TROPICAL) and the SKEB and SPPT stochastic perturbations. The ensemble consisted of more than 200 members, which was used to investigate effect on the spread in an ensemble of 20 members by combining different ensemble members. Results indicated that the initial setup to generate the ensemble did generate sufficient variance near the surface, but insufficient variance in the free troposphere, lower stratosphere. This means that in these atmospheric layers the cost function will be dominated by the background, resulting in a low sensitivity of observations. Initial results with an assimilation experiment using an ensemble generated by a single physical package, multiple initial conditions and limited stochastic perturbations through the SKEB and SPPT method resulted in a significant stronger dependency on the assimilation dataset. Experiments are ongoing, and new results will be posted shortly.

Vertical variance of u, v, T and q as function of ensemble composition
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Project Background

At present a large fraction of electricity is produced by wind-power plants. This energy is traded on the so-called spot market. An accurate estimation of the expected energy production is a key input parameter for the trading. An estimation of the expected available electricity is also an important input paramter, to balance the distribution the net, and it is also an important parameter to decide on the future need of electricity generated by conventional power plants.

The estimation of the future energy production by renewable energy sources (wind, solar) depends on the quality and completeness of the wind and solar flux forecast at high spatial and temporal resolution. The focus of the current project is with the quality and completeness of the wind forecast.

To generate a wind forecast at spatial scales of approximately 1 km, and temporal scales of about 30 min, a regional scale Numerical Weather Prediction (NWP) system is needed. Global scale NWP models, can not resolve the atmospheric phenomena at these scales accurately. As these regional scale NWP are used by smaller meteorological institutes or research organisations, the use of observations to initialise these models is limited because of the limited resources available at these centers. In particular the use of satellite observations in regional scale models is not wide spread because of these limitations.

The current project aims to better understand the value of satellite observations to characterise the initial state adopted by the regional scale NWP forecast system. In particular, observations by hyperspectral infrared sounders instruments like IASI, CrIS in combination with direct wind observations by the Aeolus mission.

These observations might be very important to characterise the atmospheric state in regions which are currently not that well sampled by either the surface based stations or aircraft observations. Hence we focus here on the Northern part of the Atlantic Ocean. The atmospheric state over this regions is important for the weather in the north-west Europe region, in particular the North Sea, where a large number of wind-power stations are located.

Hypothesis:

It is expected that by exploitation of the current constellation of hyperspectral infrared satellite observations in combination with direct wind observations by the Aeolus mission, the description of the actual weather situation can be improved, which should lead to an improved quality and completeness of the short range regional scale forecast by the regional scale numerical weather model.

Study Objective

The objectives of the Wind for renewable energy production prediction project are

  • Prepare an updated regional analysis using products derived from hyperspectral infrared, and from Aeolus Aladin observations in a regional scale assimilation system,

  • Demonstrate the added value of this updated regional analysis for the generation of a high spatial and temporal resolution wind forecast, and

  • Document the possible impact of this forecast on the prediction of renewable energy production.

Study Summary

The project has been finished in August 2023 with a final review. The relevant final documents have been distributed to ESA POC. The results are encouraging, and can be summarised as follows:

The use of Aeolus HLOS and hyperspectral infrared sounder observations in combination or separate have an impact on the analysis of atmospheric dynamics, especially in the data void regions. Especially the impact of the Hyperspectral infrared transformed retrievals assimilation is notable in the stratospheric energy distribution across spatial scales.

It is believed that the adopted method to generate the ensemble is not optimal to generate an small ensemble with sufficient spread. This could be the main reason that the impact of Aeolus HLOS and/or Hyperspectral infrared sounder observations is ambigeous in a statistical statistical sense. It is also believed that the vertical grid of the deterministic model, is not optimal to monitor near surface atmospheric conditions.

The meteorological situation considered for the study, is dominated by large scale dynamics, and hence it can be expected that therefore a better description of local conditions, will have a marginal effect on the analysis.

It is therefore recommended to

  • Improve the ensemble generation method to allow for a larger spread of short term forecasts. This could be obtained by using multiple initial conditions.

  • Improve the configuration of the WRF, in particular more vertical levels, with the first level closer to the surface.

  • Add surface wind observations by active microwave instruments like SAR or Scatterometer to the assimilation dataset, to better characterise the surface conditions. This will be important for coastal applications considered here.

  • Apply the system to a meteorological situation dominated by local conditions. For instance a summer convective situation.

Web Page Guide

Living Document

Although the ESA project has been finished, efforts along the line described above are on going. Thus the content of this web-page will still change over time, results will be consolidated and expanded when needed. At the bottom of the page, update information is provided.

Contact

This web page is made to facilitate communication with scientist working on similar projects or interested third parties. For further information, please send a communication to: info_aer_europe@aer.com. We like to hear from you.

Disclaimer

This website does not contain or collect any personal data. All information contained is located within the public record. Any user who shares information with us, shall do so at their own risk.

Acknowledgement

This project is funded by ESA through contract 4000136600/21/I-DT-Ir under the Future EO-1 EO Science for Society programme. Also at the bottom it is stated that the page is created using Sphinx. Without this tool, this web-page would not exists. The numerous contributions to the Sphinx tool is acknowledged.

Study Setup

Assimilation and Forecast System

Assimilation Experiments

General description

Before the discussion of the assimilation experiments, a general description of these experiments is provided. This concerns the adopted data sets (including source) for the experiments, the cycling through time, the region and time period.

August 2019 Experiment

This section of the document describes the results of the August 2019 Experiment. First a few characteristics of the input data is provided, followed by a brief description of the analysis results before the OSE results are presented. The section concludes with results of the deterministic forecast runs and a validation.

December 2019 Experiment

This section describes the results for the period 1 - 7 Dec 2019. The description focus more on the validation results than on other aspects of the experiment as this has been covered the section describing the August 2019 Experiment.

Scale Analysis

Here results of a preliminary spatial scale analysis applied to the background and analysis of an assimilation experiment..

Spatial Scale Analysis

Discussion, Outlook and Conclusions

The wrap-up section of the contains a discussion of the results, an outlook of potential of an operational implementation, and finally a summary and conclusions section.

Appendix

The Multiple Platform Product ExTraction (MUPPET) package used to derive the transformed retrievals is briefly described.

References

Practical Information

Hands-on experience with setting up the processing facility, compiling and running the WRF system.